Data Science & Quantitative Analysis Expert

DESCRIPTION

A leading AI lab is building the next generation of agentic evaluation benchmarks for frontier models and needs experienced data scientists and quantitative analysts to act as ground-truth experts. You will design complex analysis tasks that simulate real research work — for example, comparing two anomaly-detection algorithms on a dataset, calculating correlations, performing manual spot checks, and summarizing the findings in a notebook clear enough to drive a researcher's decision. Each task represents one to two days of continuous, focused effort and spans multiple skills: data cleaning, statistical analysis, interpretation, and clear reporting. You will work in a tight feedback loop with the lab's researchers, verifying exactly where and why frontier models fall short on rigorous analytical work. This is a full-time W-2 employment position with Cincinnatus LLC, with the opportunity to be placed at a leading AI lab as part of their extended workforce. This role is fully remote within the United States, at approximately 35 hours per week. 2. Key Responsibilities Design tasks: Create realistic data-analysis challenges — cleaning messy data, comparing methods, interpreting results — based on the kind of analysis you do every day. Author notebooks: Work through your own tasks in Jupyter or Colab, producing clear, reproducible reference analyses. Compare methods: Build tasks that ask for a fair comparison between analytical approaches, backed by spot checks and a clear recommendation. Evaluate models: Review how models handle your tasks, and check whether their statistics and conclusions actually hold up. Work as a team: Compare notes with researchers and fellow experts to keep evaluations consistent and accurate. 3. Core

REQUIREMENTS

• ▸MSc or PhD in statistics, data science, or another quantitative STEM field, or equivalent practical experience in a research-heavy analytical domain.
• ▸1+ years of experience in a research, research-engineering, or heavy data-analysis role.
• ▸Deep hands-on data-analysis skills: data cleaning, statistical correlation, hypothesis testing, and careful interpretation of results.
• ▸Proficiency with Jupyter Notebooks or Google Colab for analysis and reporting.
• ▸Working proficiency in Python (pandas, NumPy, or similar) and Git.
• ▸Strong ability to communicate analytical findings in writing for decision-makers.
• ▸Past experience in AI training, model evaluation, or benchmark/task authoring is preferred.
• ▸A perfectionist mindset: high attention to detail, creativity in task design, and the ability to work independently through ambiguous, open-ended problems.

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